A sequential pruning strategy for the selection of the number of states in hidden Markov models
Pattern Recognition Letters
A Model Selection Criterion for Classification: Application to HMM Topology Optimization
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
A speech and character combined recognition engine for mobile devices
EUC'06 Proceedings of the 2006 international conference on Embedded and Ubiquitous Computing
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This paper addresses the problem of hidden Markov model (HMM) topology estimation in the context of on-line handwriting recognition. HMM have been widely used in applications related to speech and handwriting recognition with great success. One major drawback with these approaches, however, is that the techniques that they use for estimating the topology of the models (number of states, connectivity between the states and the number of Gaussians), are usually heuristically derived, without optimal certainty. This paper addresses this problem, by comparing a couple of commonly used heuristically derived methods to an approach that uses the Bayesian information criterion (BIC) for computing the optimal topology. Experimental results on discretely written letters show that using BIC gives comparable results to heuristic approaches with a model that has nearly 10% fewer parameters.